ABSTRACT
Morphological variation of the skull was examined in the northern treeshrew (Tupaia belangeri) from various localities across Southeast Asia. Through a multivariate analysis, the treeshrews from South Vietnam exhibited distinct morphological characteristics compared to other populations from Thailand and Laos, and Malaysia. The plots of the specimens of North Vietnam are not randomly mixed with Thailand plots segregation in the scatteregrams of canonical discriminant analysis. Since the skulls of the population from North Vietnam were morphologically similar to those form central Laos and northern and northeastern Thailand, the zoogeographical barrier effect of Mekong River was not clearly confirmed. The population of the Kanchanaburi in western Thailand is clearly smaller in size compared to the other populations. The southern border of the distribution of this species is determined by the Isthmus of Kra or Kangar-Pattani Line. In the northern treeshrew, which is distributed from southern China to Bangladesh and southern Thailand, we have detected osteometrical geographical variation driven by geography. These results indicate that the skull morphology in the Tupaia glis-belangeri complex distinctively differs in South Vietnam, western Thailand, and southern Thailand. The zoogeographical barrier and factor separating these districts are expected to clarify in the future.
Subject(s)
Skull , Tupaia , Animals , Malaysia , Thailand , VietnamABSTRACT
BACKGROUND: The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. METHODS: In this study we have compiled a list of 636 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). RESULTS: Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639-peptide possibilities to 87 peptides that were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Through stringent p-value cutoff combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. CONCLUSION: We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from patient samples. We also contend that samples harvested from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.
ABSTRACT
The Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc. In this study we have compiled a list of 639 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV). Using PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639 peptide possibilities to 87 peptides which were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Applying stringent statistical scoring thresholds, combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively. We propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from a variety of sample types. We also contend that samples taken from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.
ABSTRACT
metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ ("metatranscriptomics to metaQuantome"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.
Subject(s)
Microbiota , Proteomics , Mass Spectrometry , Metagenomics , SoftwareABSTRACT
World production of farmed crustaceans was 7.8 million tons in 2016. While only making up approximately 10% of world aquaculture production, crustaceans are generally high-value species and can earn significant export income for producing countries. Viet Nam is a major seafood producing country earning USD 7.3 billion in 2016 in export income with shrimp as a major commodity. However, there is a general lack of genomic resources available for shrimp species, which is challenging to obtain due to the need to deal with large repetitive genomes, which characterize many decapod crustaceans. The first tiger prawn (P. monodon) genome assembly was assembled in 2016 using the standard Illumina PCR-based pair-end reads and a computationally-efficient but relatively suboptimal assembler, SOAPdenovo v2. As a result, the current P. monodon draft genome is highly fragmented (> 2 million scaffolds with N50 length of <1000 bp), exhibiting only moderate genome completeness (< 35% BUSCO complete single-copy genes). We sought to improve upon the recently published P. monodon genome assembly and completeness by generating Illumina PCR-free pair-end sequencing reads to eliminate genomic gaps associated with PCR-bias and performing de novo assembly using the updated MaSurCA de novo assembler. Furthermore, we scaffolded the assembly with low coverage Nanopore long reads and several recently published deep Illumina transcriptome paired-end sequencing data, producing a final genome assembly of 1.6 Gbp (1,211,364 scaffolds; N50 length of 1982 bp) with an Arthropod BUSCO completeness of 96.8%. Compared to the previously published P. monodon genome assembly from China (NCBI Accession Code: NIUS01), this represents an almost 20% increase in the overall BUSCO genome completeness that now consists of more than 90% of Arthropod BUSCO single-copy genes. The revised P. monodon genome assembly (NCBI Accession Code: VIGR01) will be a valuable resource to support ongoing functional genomics and molecular-based breeding studies in Vietnam.